A Generic Fishery Simulator
Ernesto Carrella
June 6, 2016
Fisheries today
- Worm et al(2003): fishless seas by 2050
- About 30% of the world fisheries have collapsed
- The need for policy
A brief history in failure
- Failures of the Commons and Gordon, 1954
- Effort control and capital stuffing
- Gear regulations and efficiency drops
- Area closures and fishing the lines
- Quotas and its allocation
Objectives
- Policy Simulator
- Agents Flexibility
- Model Flexibility
The State of the Art
- Random Utility Models
- Statistically Efficient
- Easily Generalizable
- Policy-Brittle
- Dynamic Programming
- Strongly Rational
- Computationally Expensive
- Ad hoc
The One Agent Problem
- Find the most profitable spot to fish
- Constraints:
- No biomass information
- Environment changes over time
- Subproblems:
- How to explore
- Explore-Exploit Tradeoff
Fast and Frugal Adaptation
- How to explore
- Tow at a nearby cell from subjective best
- Random Hill-Climbing
- When to explore
- Stochastically choose to explore next trip with probability \(p\)
- Adjust \(p\) if exploration is often (un)succesful
- Why not more nuanced bandit algorithms?
- Why not interview grounded algorithms?
Multiple Agents
- Other boats consume biomass
- You can use other boats information
- How to imitate?
- With probability \(p\) explore, with probability \(i\) ask a friend otherwise exploit
Imitation-Exploration Tradeoff
Imitation-Exploration Tradeoff
